Wireless sensor networks (WSNs) have been an emerging technology over the last decade and are expected to have major societal, environmental and financial impacts over the next few years, with more than 50 million interconnected nodes worldwide by 2020. The rapidly increasing range of applications for WSNs includes continuous human health tracking, smart buildings automation and monitoring of industrial infrastructures.
WSNs typically include multiple spatially-distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, and pressure, amongst others. The sensors in a WSN cooperatively pass their data through the network to a primary location. In some examples a WSN may be bi-directional to facilitate control of multiple spatially-distributed actuators, in tandem with receiving information on physical or environmental conditions from spatially-distributed sensors. FIGS. 1A through 1D illustrate various examples of Wireless Sensor Networks (WSNs), including a body area network (FIG. 1A), a smart building network (FIG. 1B), a smart city network (FIG. 1C), and an industrial network (FIG. 1D).
FIG. 2 illustrates a general example of a WSN that includes multiple nodes 50 coupled to a gateway 60, which is in turn coupled to a central controller or monitoring computer 70. A WSN may include relatively few nodes to several hundreds or even thousands of nodes, where each node is connected to one (or sometimes several) other nodes. FIG. 3 illustrates a general example of a WSN node 50; as can be seen in FIG. 3, a node 50 may include a signal acquisition controller 52 (e.g., an electronic circuit for interfacing with a sensor), signal processing logic 54 (e.g., to process signals derived from a sensor and acquired by the signal acquisition controller), a communications interface 56 (e.g., a radio transceiver with an internal antenna or connection to an external antenna), and an energy source 58 (e.g., a battery or an embedded form of energy harvesting). For those nodes that may also have a controlling function for an actuator, the node may include actuation logic/circuitry 59. In various implementations, sensor nodes may vary significantly in physical size and cost, depending on the complexity of the individual sensor nodes. Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and communications bandwidth.
Although different WSNs exhibit diverse requirements in terms of expected latency, throughput, energy consumption, and other aspects, a majority of these networks captures compressible analog signals at respective nodes, and aims to transmit signals reliably to one or more nodes, while maintaining low power consumption. Thus, currently, two of the main technical challenges of WSNs are achieving required communications reliability under delay constraints in usually harsh environments, and limiting their power consumption to ensure extended lifetime.
FIG. 4 illustrates, in block diagram format, a comparison of three different conventional approaches for WSN node design to acquire sparse signals, compress the acquired signals, and modulate RF carriers.
For example, the node 50(a) shown in FIG. 4 implements a common layered approach for transmission of information in WSNs, justified by Nyquist's sampling criterion and Shannon's separation theorem, in which signal acquisition (via analog-to-digital converter ADC), compression of sources (source coding), and reliable information transmission (channel coding) are decoupled. Source coding techniques compress acquired data by transforming them to other domains and/or exploiting statistical source properties. Channel coding techniques insert redundancy in transmitted data for increased communications reliability in the presence of channel noise, and their incorporation in WSNs is considered in numerous works. Most conventional PHY forward error correction (FEC) channel coding schemes operate without knowledge of the source, protecting equally every bit. Unequal error protection (UEP) schemes weight the assignment of additional resources, e.g. power, frequency or rate redundancy, to each bit depending on its relative importance, but their adoption in WSNs is limited, mainly due to the high computational complexity and application specific nature of UEP schemes.
Rapid fluctuations in the quality of a wireless medium (e.g., caused by such factors as environmental mobility and external interference) often deteriorate performance of channel coding schemes with fixed coding rate, resulting in a behavior usually known as “threshold effect.” Channel estimation and rate adaptation techniques partially address this behavior by adjusting transmission parameters depending on the experienced channel, but they are limited by the fundamental trade-off of channel quality tracking versus transmission signal energy. In some instances, rateless coding schemes have been proposed as an alternative approach without requiring feedback information, while in other proposals some cross-layer schemes provide a wider range of operational channel SNR.
Node 50(b) in FIG. 4 illustrates another conventional approach involving joint source-channel coding (JSCC) schemes. These schemes simultaneously compress and enhance the reliability of the acquired information against channel errors. In certain scenarios, these schemes may achieve superior performance compared to layered coding schemes (as illustrated by the node 50(a)), or the same performance but with significantly less delay and complexity. For instance, in the non-asymptotic regime, it has been shown that the error exponent of JSCC outperforms layered coding, and considerable advantages are associated with it in point-to-point and multiuser scenarios. Although these schemes might guarantee gracefully degrading quality of received information and lower complexity than the layered approach, they are usually signal-specific approaches and require fully analog or hybrid systems with several implementation challenges.
Node 50(c) in FIG. 4 illustrates yet another conventional approach involving “compressed sensing.” Because a plethora of naturally occurring signals (e.g., images and human biosignals), as well as several signals in practical systems (e.g., radio frequency (RF) signals in wireless receivers), exhibit high sparsity levels or can be sufficiently approximated by sparse models, researchers have proposed specific coding schemes to represent and efficiently process efficiently sparse signals. Generally speaking, a sparse signal is a signal that can be represented, in at least one domain (e.g., time domain, frequency domain), by a few representation coefficients (e.g., significantly fewer than the signal's dimensionality).
Compressed sensing (CS) is such a scheme for efficient acquisition of sparse signals, based on random projections and incoherent sampling. Although most nodes with CS acquisition, as the node 50(c) in FIG. 4, have several advantages, e.g., signal independent operation and lower complexity, they inherit the typical limitations of PHY FEC schemes, require channel state information (CSI) for appropriate rate selection and do not perform well in multiuser scenarios.